Daniel Fehrle, Christopher Heiberger, Johannes Huber
Polynomial
chaos expansion: Efficient evaluation and estimation of computational
models
Abstract:
Polynomial
chaos expansion (PCE) provides a method that enables the user to
represent a quantity of interest (QoI) of a model’s solution
as a
series expansion of uncertain model inputs, usually its parameters.
Among the QoIs are the policy function, the second moments of
observables, or the posterior kernel. Hence, PCE sidesteps the repeated
and time consuming evaluations of the model’s outcomes. The
paper
discusses the suitability of PCE for computational economics. We,
therefore, introduce to the theory behind PCE, analyze the convergence
behavior for different elements of the solution of the standard real
business cycle model as illustrative example, and check the accuracy,
if standard empirical methods are applied. The results are promising,
both in terms of accuracy and efficiency.
JEL: C11, C13, C32, C63
Paper:
Paper available as pdf-file.
Beitrag Nr. 341, Volkswirtschaftliche Diskussionsreihe, Institut
für
Volkswirtschaftslehre der Universität Augsburg
Contact:
Daniel
Fehrle, University of Augsburg, Department of Economics,
D-86135
Augsburg,
Germany, phone +49-821-598-4201, fax +49-821-598-4231
email: daniel.fehrle@wiwi.uni-augsburg.de
Christopher
Heiberger, University of Augsburg, Department of
Economics, D-86135
Augsburg,
Germany, phone +49-821-598-4189, fax +49-821-598-4231
email: Christopher.Heiberger@wiwi.uni-augsburg.de
Johannes Huber,
University of Augsburg, Department of Economics,
D-86135
Augsburg,
Germany, phone +49-821-598-4076, fax +49-821-598-4231
email: johannes.huber@wiwi.uni-augsburg.de
Bo.,
17.12.2020